In 1945 the volume of human knowledge doubled every 25 years. Now, that number is 12 hours [1]. With our collective computational power rapidly increasing, vast amounts of data and our ability to assimilate it, has seeded unprecedented fertile ground for innovation. Healthtech companies are rapidly sprouting from data ridden soil at exponential rates. Cell free DNA companies, once a rarity, are becoming ubiquitous. The genomics landscape, once dominated by the few, are being inundated by a slew of competitors. Grandiose claims of being able to diagnose 50 different cancers from a single blood sample, or use AI to best dermatologists, radiologists, pathologists, etc., are being made at alarming rates. Accordingly, it's imperative to know how to assess these claims as fact or fiction, particularly when such claimants may employ "statistical misdirection". In this addition to "The Insider's Guide to Translational Medicine" we disarm perpetrators of statistical warfare of their greatest ...

A new initiative to partner with communities working with artificial intelligence and emerging technologies will expand efforts of promoting intellectual property protection in these areas, the US Patent and Trademark Office hopes. In a Federal Register notice published on Monday, the PTO said it is focused on incentivizing more innovation in key technology areas such as AI and ET--which includes quantum computing, synthetic biology, blockchain, and virtual reality. The office announced a series of meetings intended to explore "AI/ET-related initiatives at the USPTO and IP policy issues impacted by AI and other ET." The intitiative will further the PTO's alignment ...

The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very coordinated manner to perpetuate sophisticated attacks that could bring down the entire network and many critical hosts in the network. Increasingly advanced deep and machine learning-based solutions have been used in threat detection and protection. The application of these techniques has been reviewed well in the scientific literature. Deep Reinforcement Learning has shown great promise in developing AI-based solutions for areas that had earlier required advanced human cognizance. Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities. These algorithms have recently also been used in cybersecurity, especially in threat detection and endpoint protection, where these are showing state-of-the-art results. Unlike supervised machines and deep learning, deep reinforcement learning is used in more diverse ways and is empowering many innovative applications in the threat defense landscape. However, there does not exist any comprehensive review of these unique applications and accomplishments. Therefore, in this paper, we intend to fill this gap and provide a comprehensive review of the different applications of deep reinforcement learning in cybersecurity threat detection and protection.

Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are critical, such black-box AI models are not sufficient. Explainable Artificial Intelligence (XAI) addresses this problem and defines a set of AI models that are interpretable by the users. Recently, several number of XAI models have been to address the issues surrounding by lack of interpretability and explainability of black-box models in various application areas such as healthcare, military, energy, financial and industrial domains. Although the concept of XAI has gained great deal of attention recently, its integration into the IoT domain has not yet been fully defined. In this paper, we provide an in-depth and systematic review of recent studies using XAI models in the scope of IoT domain. We categorize the studies according to their methodology and applications areas. In addition, we aim to focus on the challenging problems and open issues and give future directions to guide the developers and researchers for prospective future investigations.

"We must develop a comprehensive global shared view of how technology is affecting our lives and reshaping the economic and social environments." Emboldened by the challenge, Nazareen Ebrahim is ready to answer the call to take on and profit amid the Fourth Industrial Revolution. Ebrahim is a senior media, communications and technology practitioner with a background in media strategy; project management; content development; training and facilitation; and radio and television presenting and production. She leads the artificial intelligence research team at Socially Acceptable, of which she is the CEO and founder. The company produced a 12-part artificial intelligence series that aims to educate professionals and the general public on artificial intelligence.

More than 750 Tesla owners have complained to U.S. safety regulators that cars operating on the automaker's partially automated driving systems have suddenly stopped on roadways for no apparent reason. The National Highway Traffic Safety Administration revealed the number in a detailed information request letter to Tesla that was posted Friday on the agency's website. The 14-page letter dated May 4 asks the automaker for all consumer and field reports it has received about false braking, as well as reports of crashes, injuries, deaths and property damage claims. It also asks whether the company's "Full Self Driving" and automatic emergency braking systems were active at the time of any incident. The agency began investigating phantom braking in Tesla's Models 3 and Y last February after getting 354 complaints.

There's a lot of talent in the guard and reserve forces that the Department of Defense (DOD) could be using but is not aware of such as reservists in their civilian jobs might be working on cloud computing, software engineering, cybersecurity, or any number of other in-demand skills, according to Scott Sumner, technical project manager at the Defense Innovation Unit's Artificial Intelligence/Machine Learning Portfolio. The DOD is hampered in its efforts to locate these individuals because it does not possess the resources necessary to do so. The department will be able to look for the appropriate matches with the assistance of Gig Eagle's AI-powered app. The platform will take into consideration the reservist's skill preferences as well as the biographical information that they enter the app, including their existing skill sets. The algorithm that controls the AI will focus on terms that are similar and imply or infer a specific talent or expertise.

According to the Global Economic Crime and Fraud Report conducted by the global audit firm PwC, financial fraud and cybercrime hit an all-time high this year. In fact, in the past two years, 49% of international organizations reported experiencing economic fraud. While numerous institutions are introducing new technologies to eradicate crime, technology to prevent economic crime and fraud with artificial intelligence is attracting attention. According to a report by PwC, there are three common types of economic crime and fraud. These are asset theft, cybercrime, and consumer fraud.

Cybersecurity is the overall activity of protecting computers, networks, and data from malicious electronic attacks. It is an activity that compares to physical security, a more traditional security activity that controls access to buildings or other objects in the real world. While many high-tech physical security vendors have a combination of physical and cybersecurity in their org charts, cybersecurity is an activity that focuses on protecting assets from malicious logins and code, not on property intrusion or theft. Cybersecurity is a broad concept that encompasses several specific fields of activity. There are many classification methods. For example, there is Kaspersky Lab's classification system, and Mindcore has such a system.

Executives at facial recognition firm Clarifai may have sighed with relief in March 2021 when a judge agreed that they could not be sued for violating Illinois' biometric privacy law, but then the federal government came knocking. The Federal Trade Commission now wants to know how the face image that a woman posted on the OkCupid dating site ended up being used as training data by Clarifai without her consent or disclosing the transaction as required by Illinois' Biometric Information Privacy Act. Clarifai makes computer vision, deep learning AI and biometrics systems. Claiming that its investigation is being stonewalled by Match Group, owner of OkCupid, the FTC has filed suit (case number 1:22-mc-00054), according to Bloomberg Law. The government claims that OkCupid engaged in unfair and deceptive conduct by sharing biometric data with Clarifai in 2014.